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Sahoo K, Sundararajan V. Methods in DNA methylation array dataset analysis: A review. Comput Struct Biotechnol J 2024; 23:2304-2325. [PMID: 38845821 PMCID: PMC11153885 DOI: 10.1016/j.csbj.2024.05.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 04/25/2024] [Accepted: 05/08/2024] [Indexed: 06/09/2024] Open
Abstract
Understanding the intricate relationships between gene expression levels and epigenetic modifications in a genome is crucial to comprehending the pathogenic mechanisms of many diseases. With the advancement of DNA Methylome Profiling techniques, the emphasis on identifying Differentially Methylated Regions (DMRs/DMGs) has become crucial for biomarker discovery, offering new insights into the etiology of illnesses. This review surveys the current state of computational tools/algorithms for the analysis of microarray-based DNA methylation profiling datasets, focusing on key concepts underlying the diagnostic/prognostic CpG site extraction. It addresses methodological frameworks, algorithms, and pipelines employed by various authors, serving as a roadmap to address challenges and understand changing trends in the methodologies for analyzing array-based DNA methylation profiling datasets derived from diseased genomes. Additionally, it highlights the importance of integrating gene expression and methylation datasets for accurate biomarker identification, explores prognostic prediction models, and discusses molecular subtyping for disease classification. The review also emphasizes the contributions of machine learning, neural networks, and data mining to enhance diagnostic workflow development, thereby improving accuracy, precision, and robustness.
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Affiliation(s)
| | - Vino Sundararajan
- Correspondence to: Department of Bio Sciences, School of Bio Sciences and Technology, Vellore Institute of Technology, Vellore 632 014, Tamil Nadu, India.
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2
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Qu Z, Nguyen QV, Lau CW, Johnston A, Kennedy PJ, Simoff S, Catchpoole D. Understanding cancer patient cohorts in virtual reality environment for better clinical decisions: a usability study. BMC Med Inform Decis Mak 2023; 23:295. [PMID: 38124044 PMCID: PMC10731816 DOI: 10.1186/s12911-023-02392-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND Visualising patient genomic data in a cohort with embedding data analytics models can provide relevant and sensible patient comparisons to assist a clinician with treatment decisions. As immersive technology is actively used around the medical world, there is a rising demand for an efficient environment that can effectively display genomic data visualisations on immersive devices such as a Virtual Reality (VR) environment. The VR technology will allow clinicians, biologists, and computer scientists to explore a cohort of individual patients within the 3D environment. However, demonstrating the feasibility of the VR prototype needs domain users' feedback for future user-centred design and a better cognitive model of human-computer interactions. There is limited research work for collecting and integrating domain knowledge into the prototype design. OBJECTIVE A usability study for the VR prototype--Virtual Reality to Observe Oncology data Models (VROOM) was implemented. VROOM was designed based on a preliminary study among medical users. The goals of this usability study included establishing a baseline of user experience, validating user performance measures, and identifying potential design improvements that are to be addressed to improve efficiency, functionality, and end-user satisfaction. METHODS The study was conducted with a group of domain users (10 males, 10 females) with portable VR devices and camera equipment. These domain users included medical users such as clinicians and genetic scientists and computing domain users such as bioinformatics and data analysts. Users were asked to complete routine tasks based on a clinical scenario. Sessions were recorded and analysed to identify potential areas for improvement to the data visual analytics projects in the VR environment. The one-hour usability study included learning VR interaction gestures, running visual analytics tool, and collecting before and after feedback. The feedback was analysed with different methods to measure effectiveness. The statistical method Mann-Whitney U test was used to analyse various task performances among the different participant groups, and multiple data visualisations were created to find insights from questionnaire answers. RESULTS The usability study investigated the feasibility of using VR for genomic data analysis in domain users' daily work. From the feedback, 65% of the participants, especially clinicians (75% of them), indicated that the VR prototype is potentially helpful for domain users' daily work but needed more flexibility, such as allowing them to define their features for machine learning part, adding new patient data, and importing their datasets in a better way. We calculated the engaged time for each task and compared them among different user groups. Computing domain users spent 50% more time exploring the algorithms and datasets than medical domain users. Additionally, the medical domain users engaged in the data visual analytics parts (approximately 20%) longer than the computing domain users.
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Affiliation(s)
- Zhonglin Qu
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia.
| | - Quang Vinh Nguyen
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
| | - Chng Wei Lau
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
| | - Andrew Johnston
- School of Computer Science, University of Technology Sydney, Sydney, Australia
| | - Paul J Kennedy
- School of Computer Science, University of Technology Sydney, Sydney, Australia
| | - Simeon Simoff
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Sydney, Australia
| | - Daniel Catchpoole
- School of Computer Science, University of Technology Sydney, Sydney, Australia.
- Biospecimen Research Services, Children's Cancer Research Unit, The Kids Research Institute, The Children's Hospital at Westmead, Sydney, Australia.
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Seitz W, Kirwan AD, Brčić-Kostić K, Mitrikeski PT, Seitz PK. Visualizing genomic data: The mixing perspective. Biosystems 2023; 224:104839. [PMID: 36690200 DOI: 10.1016/j.biosystems.2023.104839] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 01/22/2023]
Abstract
We report on a novel way to visualize genomic data. By considering genome coding sequences, cds, as sets of the N=61 non-stop codons, one obtains a partition of the total number of codons in each cds. Partitions exhibit a statistical property known as mixing character which characterizes how mixed the partition is. Mixing characters have been shown mathematically to exhibit a partial order known as majorization (Ruch, 1975). In previous work (Seitz and Kirwan, 2022) we developed an approach that combined mixing and entropy that is visualized as a scatter plot. If we consider all 1,121,505 partitions of 61 codons, this produces a plot we call the theoretical mixing space, TGMS. A normalization procedure is developed here and applied to real genomic data to produce the genome mixing signature, GMS. Example GMS's of 19 species, including Homo sapiens, are shown and discussed.
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Affiliation(s)
- William Seitz
- Texas A&M University at Galveston, Galveston, TX 77553, United States of America.
| | - A D Kirwan
- College of Earth, Ocean and Environment, University of Delaware, Newark, DE, 19716, United States of America
| | - Krunoslav Brčić-Kostić
- Laboratory of Evolutionary Genetics, Division of Molecular Biology, Ruđer Bos̆ković Institute, Zagreb 10000, Croatia
| | - Petar Tomev Mitrikeski
- Laboratory of Evolutionary Genetics, Division of Molecular Biology, Ruđer Bos̆ković Institute, Zagreb 10000, Croatia
| | - P K Seitz
- University of Texas Medical Branch, Galveston, TX 77555, United States of America
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Lau CW, Qu Z, Draper D, Quan R, Braytee A, Bluff A, Zhang D, Johnston A, Kennedy PJ, Simoff S, Nguyen QV, Catchpoole D. Virtual reality for the observation of oncology models (VROOM): immersive analytics for oncology patient cohorts. Sci Rep 2022; 12:11337. [PMID: 35790803 PMCID: PMC9256599 DOI: 10.1038/s41598-022-15548-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 06/24/2022] [Indexed: 11/08/2022] Open
Abstract
The significant advancement of inexpensive and portable virtual reality (VR) and augmented reality devices has re-energised the research in the immersive analytics field. The immersive environment is different from a traditional 2D display used to analyse 3D data as it provides a unified environment that supports immersion in a 3D scene, gestural interaction, haptic feedback and spatial audio. Genomic data analysis has been used in oncology to understand better the relationship between genetic profile, cancer type, and treatment option. This paper proposes a novel immersive analytics tool for cancer patient cohorts in a virtual reality environment, virtual reality to observe oncology data models. We utilise immersive technologies to analyse the gene expression and clinical data of a cohort of cancer patients. Various machine learning algorithms and visualisation methods have also been deployed in VR to enhance the data interrogation process. This is supported with established 2D visual analytics and graphical methods in bioinformatics, such as scatter plots, descriptive statistical information, linear regression, box plot and heatmap into our visualisation. Our approach allows the clinician to interrogate the information that is familiar and meaningful to them while providing them immersive analytics capabilities to make new discoveries toward personalised medicine.
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Affiliation(s)
- Chng Wei Lau
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia.
| | - Zhonglin Qu
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | | | - Rosa Quan
- School of Psychology, Western Sydney University, Penrith, Australia
| | - Ali Braytee
- School of Computer Science, University of Technology Sydney, Ultimo, Australia
| | | | - Dongmo Zhang
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | - Andrew Johnston
- School of Computer Science, University of Technology Sydney, Ultimo, Australia
| | - Paul J Kennedy
- School of Computer Science, University of Technology Sydney, Ultimo, Australia
| | - Simeon Simoff
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | - Quang Vinh Nguyen
- MARCS Institute and School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia
| | - Daniel Catchpoole
- School of Computer, Data and Mathematical Sciences, Western Sydney University, Parramatta, Australia.
- School of Computer Science, University of Technology Sydney, Ultimo, Australia.
- Biospecimen Research Services, Children's Cancer Research Unit, The Kids Research Institute, The Children's Hospital at Westmead, Westmead, Australia.
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Xin Z, Cai Y, Dang LT, Burke HMS, Revote J, Charitakis N, Bienroth D, Nim HT, Li YF, Ramialison M. MonaGO: a novel gene ontology enrichment analysis visualisation system. BMC Bioinformatics 2022; 23:69. [PMID: 35164667 PMCID: PMC8845231 DOI: 10.1186/s12859-022-04594-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Accepted: 01/31/2022] [Indexed: 11/12/2022] Open
Abstract
Background Gene ontology (GO) enrichment analysis is frequently undertaken during exploration of various -omics data sets. Despite the wide array of tools available to biologists to perform this analysis, meaningful visualisation of the overrepresented GO in a manner which is easy to interpret is still lacking. Results Monash Gene Ontology (MonaGO) is a novel web-based visualisation system that provides an intuitive, interactive and responsive interface for performing GO enrichment analysis and visualising the results. MonaGO supports gene lists as well as GO terms as inputs. Visualisation results can be exported as high-resolution images or restored in new sessions, allowing reproducibility of the analysis. An extensive comparison between MonaGO and 11 state-of-the-art GO enrichment visualisation tools based on 9 features revealed that MonaGO is a unique platform that simultaneously allows interactive visualisation within one single output page, directly accessible through a web browser with customisable display options. Conclusion MonaGO combines dynamic clustering and interactive visualisation as well as customisation options to assist biologists in obtaining meaningful representation of overrepresented GO terms, producing simplified outputs in an unbiased manner. MonaGO will facilitate the interpretation of GO analysis and will assist the biologists into the representation of the results. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04594-1.
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Affiliation(s)
- Ziyin Xin
- Faculty of IT, Monash University, Clayton, VIC, Australia
| | - Yujun Cai
- Faculty of IT, Monash University, Clayton, VIC, Australia.,Southeast University, Nanjing, China
| | - Louis T Dang
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia.,Systems Biology Institute Australia, Clayton, VIC, Australia
| | - Hannah M S Burke
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia.,Systems Biology Institute Australia, Clayton, VIC, Australia
| | - Jerico Revote
- Monash eResearch Centre, Monash University, Melbourne, VIC, Australia
| | | | - Denis Bienroth
- Murdoch Children's Research Institute, Parkville, VIC, Australia
| | - Hieu T Nim
- Faculty of IT, Monash University, Clayton, VIC, Australia. .,Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia. .,Systems Biology Institute Australia, Clayton, VIC, Australia. .,Murdoch Children's Research Institute, Parkville, VIC, Australia.
| | - Yuan-Fang Li
- Faculty of IT, Monash University, Clayton, VIC, Australia. .,Systems Biology Institute Australia, Clayton, VIC, Australia.
| | - Mirana Ramialison
- Australian Regenerative Medicine Institute, Monash University, Clayton, VIC, Australia. .,Systems Biology Institute Australia, Clayton, VIC, Australia. .,Murdoch Children's Research Institute, Parkville, VIC, Australia.
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Skidmore ZL, Campbell KM, Cotto KC, Griffith M, Griffith OL. Exploring the Genomic Landscape of Cancer Patient Cohorts with GenVisR. Curr Protoc 2021; 1:e252. [PMID: 34506690 PMCID: PMC8439545 DOI: 10.1002/cpz1.252] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
The creation of visualizations to interpret genomics data remains an important aspect of data science within computational biology. The GenVisR Bioconductor package was created to lower the entry point for publication-quality graphics and has remained a popular suite of tools within this domain. GenVisR supports visualizations covering a breadth of topics including functions to produce visual summaries of copy-number alterations, somatic variants, sequence quality metrics, and more. Recently, the GenVisR package has undergone significant updates to increase performance and functionality. To demonstrate the utility of GenVisR, we present protocols for use of the updated Waterfall() function to create a customizable Oncoprint-style plot of the mutational landscape of a tumor cohort. We explain the basics of installation, data import, configuration, plotting, clinical annotation, and customization. A companion online workshop describing the GenVisR library, Waterfall() function, and other genomic visualization tools is available at genviz.org. © 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Generating a Waterfall() plot from original mutation data Basic Protocol 2: Adding clinical data to a Waterfall() plot Basic Protocol 3: Customizing mutation burden in Waterfall() plots Basic Protocol 4: Brief exploration of customizable options Support Protocol 1: Installing GenVisR.
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Affiliation(s)
- Zachary L. Skidmore
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Katie M. Campbell
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Kelsy C. Cotto
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
| | - Malachi Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA
| | - Obi L. Griffith
- Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA
- McDonnell Genome Institute, Washington University School of Medicine, St Louis, MO, USA
- Department of Genetics, Washington University School of Medicine, St Louis, MO, USA
- Siteman Cancer Center, Washington University School of Medicine, St Louis, MO, USA
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Belciug S. Pathologist at work. Artif Intell Cancer 2020. [DOI: 10.1016/b978-0-12-820201-2.00003-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
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Nusrat S, Harbig T, Gehlenborg N. Tasks, Techniques, and Tools for Genomic Data Visualization. COMPUTER GRAPHICS FORUM : JOURNAL OF THE EUROPEAN ASSOCIATION FOR COMPUTER GRAPHICS 2019; 38:781-805. [PMID: 31768085 PMCID: PMC6876635 DOI: 10.1111/cgf.13727] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Genomic data visualization is essential for interpretation and hypothesis generation as well as a valuable aid in communicating discoveries. Visual tools bridge the gap between algorithmic approaches and the cognitive skills of investigators. Addressing this need has become crucial in genomics, as biomedical research is increasingly data-driven and many studies lack well-defined hypotheses. A key challenge in data-driven research is to discover unexpected patterns and to formulate hypotheses in an unbiased manner in vast amounts of genomic and other associated data. Over the past two decades, this has driven the development of numerous data visualization techniques and tools for visualizing genomic data. Based on a comprehensive literature survey, we propose taxonomies for data, visualization, and tasks involved in genomic data visualization. Furthermore, we provide a comprehensive review of published genomic visualization tools in the context of the proposed taxonomies.
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Affiliation(s)
- S Nusrat
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - T Harbig
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - N Gehlenborg
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
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